


'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__ = 5000_0000	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= set()
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= int((limit - 24) ** (1 / 2)	)

				_SCREAMING_SNAKE_CASE					:			Dict							= set(range(3				, prime_square_limit + 1				, 2	)	)
				primes.add(2	)
				for p in range(3				, prime_square_limit + 1				, 2	):
								if p not in primes:
												continue
								primes.difference_update(set(range(p * p				, prime_square_limit + 1				, SCREAMING_SNAKE_CASE__	)	)	)

				for primea in primes:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= primea * primea
								for primea in primes:
												_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= primea * primea * primea
												if square + cube >= limit - 16:
																break
												for primea in primes:
																_SCREAMING_SNAKE_CASE					:			Optional[Any]							= primea * primea * primea * primea
																_SCREAMING_SNAKE_CASE					:			int							= square + cube + tetr
																if total >= limit:
																				break
																ret.add(SCREAMING_SNAKE_CASE__	)

				return len(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				print(F"{solution() = }")








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  List[Any]				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Any				  =						{
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: Dict       =    """gpt_bigcode"""
							A_				: str       =    ["""past_key_values"""]
							A_				: Tuple       =    {
							    """hidden_size""": """n_embd""",
							    """max_position_embeddings""": """n_positions""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}






							def __init__(       self       ,   __snake_case=5_0257       ,   __snake_case=1024       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=None       ,   __snake_case="gelu_pytorch_tanh"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=1e-5       ,   __snake_case=0.02       ,   __snake_case=True       ,   __snake_case=True       ,   __snake_case=5_0256       ,   __snake_case=5_0256       ,   __snake_case=True       ,   __snake_case=True       ,   __snake_case=True       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= n_positions
											_SCREAMING_SNAKE_CASE					:			List[Any]							= n_embd
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= n_layer
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= n_head
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= n_inner
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= activation_function
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= resid_pdrop
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= embd_pdrop
											_SCREAMING_SNAKE_CASE					:			List[Any]							= attn_pdrop
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= layer_norm_epsilon
											_SCREAMING_SNAKE_CASE					:			Any							= initializer_range
											_SCREAMING_SNAKE_CASE					:			str							= scale_attn_weights
											_SCREAMING_SNAKE_CASE					:			Tuple							= use_cache
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= attention_softmax_in_fpaa
											_SCREAMING_SNAKE_CASE					:			List[Any]							= scale_attention_softmax_in_fpaa
											_SCREAMING_SNAKE_CASE					:			Any							= multi_query

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= bos_token_id
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= eos_token_id

											super().__init__(bos_token_id=__snake_case       ,   eos_token_id=__snake_case       ,   **__snake_case    )




